Land Cover Changes after the Massive Rohingya Refugee Influx in Bangladesh: Neo-Classic Unsupervised Approach
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. Remote Sensing Images
2.2.1. Sentinel-1 Dataset
2.2.2. Sentinel-2 Dataset
2.3. Classification Procedures
2.3.1. Classification Technique
2.3.2. Classification Steps
2.3.3. Accuracy Assessment
3. Results
3.1. Parameter Selection
3.2. Yearly Land Covers
3.3. Land Cover Changes
4. Discussion
4.1. Peculiarity of the Classification Approach
4.2. Impacts on Local Landscape
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Year Group | Sentinel-1 | Sentinel-2 |
---|---|---|
2015–2017 | 13 | 8 |
2018–2019 | 31 | 16 |
2019–2020 | 30 | 15 |
2020–2021 | 30 | 19 |
1st Step (VV and NDWI) | 2nd Step (VH and NDWI) | |||
---|---|---|---|---|
Year Group | Class Separation | Class Numbers | Class Separation | Class Numbers |
2015–2017 | 1.0 | 12 | 1.0 | 11 |
2018–2019 | 1.4 | 13 | 1.5 | 8 |
2019–2020 | 1.5 | 11 | 1.5 | 8 |
2020–2021 | 1.5 | 13 | 1.6 | 10 |
Classes | Definitions |
---|---|
Wetland/Saltpan | The areas exposed to year-around or seasonal inundation by surface water or sea water |
Agriculture/Open field | Paddy fields, crop fields, grassland, areas of sparse or seasonal vegetation |
Degraded forest/Bush | Areas with low to medium vegetation coverage, dominated by small to medium-sized bush with a sparse presence of mature trees and saplings from coppicing |
Forest | Areas of moderate to densely covered vegetation including mature trees, dense saplings, and homestead agroforestry |
Built-up/Disturbed areas | Area occupied with artificial objects such as settlements, agricultural tents, and brickfields; also, area exposed to unusual disturbance by artificial or natural events such as construction, logging, and landslides |
No data | Misjudged areas as wetlands (excluded from the analysis) |
2015–17 (OA: 74.5) | 2018–19 (OA: 80.0) | ||||||||||||
W | A | D | F | B | PA | W | A | D | F | B | PA | ||
W | 8 | 0 | 0 | 0 | 0 | 100 | W | 8 | 0 | 0 | 0 | 0 | 100 |
A | 0 | 18 | 0 | 0 | 0 | 100 | A | 2 | 20 | 0 | 0 | 0 | 90.9 |
D | 1 | 1 | 19 | 8 | 1 | 63.3 | D | 1 | 2 | 15 | 5 | 1 | 62.5 |
F | 1 | 0 | 11 | 24 | 0 | 66.7 | F | 0 | 0 | 7 | 29 | 0 | 80.6 |
B | 0 | 0 | 1 | 1 | 4 | 66.7 | B | 0 | 2 | 0 | 0 | 8 | 80.0 |
UA | 80.0 | 94.7 | 61.3 | 72.7 | 80.0 | UA | 72.7 | 83.8 | 68.2 | 85.3 | 88.9 | ||
2019–20 (OA: 80.8) | 2020–21 (OA: 85.9) | ||||||||||||
W | A | D | F | B | PA | W | A | D | F | B | PA | ||
W | 7 | 0 | 0 | 0 | 0 | 100 | W | 4 | 0 | 0 | 0 | 1 | 80.0 |
A | 1 | 21 | 0 | 0 | 0 | 95.5 | A | 1 | 16 | 1 | 0 | 0 | 88.9 |
D | 0 | 0 | 13 | 9 | 0 | 59.1 | D | 0 | 0 | 19 | 2 | 2 | 82.6 |
F | 0 | 0 | 6 | 28 | 0 | 82.4 | F | 0 | 0 | 4 | 32 | 0 | 88.9 |
B | 0 | 2 | 1 | 0 | 11 | 78.6 | B | 1 | 2 | 0 | 0 | 14 | 82.4 |
UA | 87.5 | 91.3 | 65.0 | 75.7 | 100 | UA | 66.7 | 88.9 | 79.2 | 94.1 | 82.4 |
Wetland/ Saltpan | Agriculture /Open Field | Degraded forest/Bush | Forest | Built-Up/Disturbed Areas | |
---|---|---|---|---|---|
Total | |||||
2015–17 | 4754 (7.9) | 10,984 (18.3) | 18,516 (30.8) | 22,041 (36.7) | 3789 (6.3) |
2018–19 | 4506 (7.5) | 13,137 (21.9) | 14,424 (24.0) | 22,045 (36.7) | 5965 (9.9) |
2019–20 | 3917 (6.5) | 13,462 (22.4) | 13,392 (22.3) | 20,851 (34.7) | 8387 (14.0) |
2020–21 | 2969 (4.9) | 11,011 (18.3) | 13,910 (23.1) | 21,626 (36.0) | 10,614 (17.7) |
INP | |||||
2015–17 | 16 (0.1) | 1100 (7.5) | 5422 (37.0) | 7303 (49.8) | 830 (5.7) |
2018–19 | 30 (0.2) | 1451 (9.9) | 3236 (22.1) | 7344 (50.1) | 2609 (17.8) |
2019–20 | 27 (0.2) | 1416 (9.7) | 3689 (25.2) | 6575 (44.9) | 2948 (20.1) |
2020–21 | 13 (0.1) | 814 (5.5) | 3724 (25.4) | 6988 (47.6) | 3133 (21.4) |
TWS | |||||
2015–17 | 11 (0.1) | 342 (3.0) | 4117 (36.1) | 6373 (56.0) | 547 (4.8) |
2018–19 | 19 (0.2) | 534 (4.7) | 4065 (35.6) | 6199 (54.4) | 586 (5.1) |
2019–20 | 23 (0.2) | 527 (4.6) | 4141 (36.4) | 5628 (49.4) | 1066 (9.4) |
2020–21 | 13 (0.1) | 271 (4.6) | 3905 (34.2) | 6290 (55.1) | 936 (8.2) |
Inside of camps | |||||
2015–17 | 26 (1.0) | 398 (15.6) | 1095 (43.0) | 717 (28.2) | 308 (12.1) |
2018–19 | 21 (0.8) | 512 (20.1) | 419 (16.5) | 200 (7.9) | 1395 (54.8) |
2019–20 | 16 (0.6) | 384 (15.1) | 396 (15.6) | 228 (9.0) | 1520 (59.7) |
2020–21 | 9 (0.4) | 227 (8.9) | 529 (20.8) | 159 (6.2) | 1622 (63.7) |
1 km from camps | |||||
2015–17 | 440 (6.9) | 1135 (17.7) | 2356 (36.8) | 2044 (31.9) | 430 (6.7) |
2018–19 | 417 (6.5) | 1579 (24.6) | 1796 (28.0) | 1706 (26.6) | 912 (14.2) |
2019–20 | 239 (3.7) | 1728 (27.0) | 1672 (26.1) | 1597 (24.9) | 1166 (18.2) |
2020–21 | 266 (4.1) | 1233 (19.2) | 1680 (26.2) | 1772 (27.6) | 1466 (22.8) |
2015–17 | Wetland/ Saltpan | Agriculture /Open Field | Degraded Forest/Bush | Forest | Built-Up /Disturbed Areas |
---|---|---|---|---|---|
2018–19 | 15 (0.3) | 772 (12.9) | 2554 (42.8) | 1122 (18.8) | 1502 (25.2) |
2019–20 | 29 (0.3) | 1245 (14.8) | 3921 (46.8) | 1273 (15.2) | 1916 (22.9) |
2020–21 | 106 (1.0) | 2421 (22.8) | 4941 (46.6) | 1569 (14.8) | 1573 (14.8) |
2020–21 | Wetland/ Saltpan | Agriculture /Open Field | Degraded Forest/Bush | Forest | Built-Up /Disturbed Areas |
---|---|---|---|---|---|
2015–17 | |||||
Wetland/Saltpan | 2456 | 1875 | 218 | 89 | 106 |
Agriculture/Open field | 288 | 7206 | 859 | 206 | 2421 |
Degraded forest/Bush | 105 | 1283 | 6767 | 5418 | 4941 |
Forest | 90 | 415 | 4419 | 15,540 | 1569 |
Built-up/Disturbed areas | 15 | 225 | 1633 | 341 | 1573 |
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Sakamoto, M.; Ullah, S.M.A.; Tani, M. Land Cover Changes after the Massive Rohingya Refugee Influx in Bangladesh: Neo-Classic Unsupervised Approach. Remote Sens. 2021, 13, 5056. https://doi.org/10.3390/rs13245056
Sakamoto M, Ullah SMA, Tani M. Land Cover Changes after the Massive Rohingya Refugee Influx in Bangladesh: Neo-Classic Unsupervised Approach. Remote Sensing. 2021; 13(24):5056. https://doi.org/10.3390/rs13245056
Chicago/Turabian StyleSakamoto, Maiko, Shah M. Asik Ullah, and Masakazu Tani. 2021. "Land Cover Changes after the Massive Rohingya Refugee Influx in Bangladesh: Neo-Classic Unsupervised Approach" Remote Sensing 13, no. 24: 5056. https://doi.org/10.3390/rs13245056